The Book of Why: The new science of cause and effect

Causal reasoning is at the core of everything we see, do, and imagine. Causal inference is the foundation of scientific thinking and reasoning. Every explicit decision we make is the realization of causal thinking. You will be surprised to learn that the rigorous study of causality as a science is relatively new in comparison to the disciplines of statistics and probability. The history of the Causal Revolution will surprise, inspire, entertain, and – at times – shock you! Below is my 5-star Amazon review of the Judea Pearl´s The Book of Why: The New Science of Cause and Effect (which became an Amazon ‘Best Seller’ within the first week of its release!).


Reconstructing Brain MRI Images Using Deep Learning (Convolutional Autoencoder)

In this tutorial, you’ll learn & understand how to read nifti format brain magnetic resonance imaging (MRI) images, reconstructing them using convolutional autoencoder.


Explaining Reinforcement Learning: Active vs Passive

We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.


Reinforcement Learning: An Introduction – 2nd Edition Draft Book


Image classification with keras in roughly 100 lines of code.

I have been using keras and TensorFlow for a while now – and love its simplicity and straight-forward way to modeling. As part of the latest update to my Workshop about deep learning with R and keras I’ve added a new example analysis such as Building an image classifier to differentiate different types of fruits.
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